Methodology for the immediate detection and treatment of wheel wear in contour grinding

Abstract

Contour grinding is widely used in machining different types of curved parts with contour surfaces, such as precision tools and molds, and grinding wheel wear has a crucial influence on the contour accuracy. In this study, we investigated a machine-vision-based method for real-time detection and treatment of wheel wear to overcome the disadvantages of low efficiency and manual dependence in traditional optical contour grinding. An original principle for the immediate detection of wheel wear was introduced based on the local contour image of the workpiece (LCIW), and an algorithm to online detect the wheel-wear-induced contour error was proposed. Subsequently, A LCIW-based normal tracking algorithm was developed to compensate the wheel-wear-induced contour errors in real time. We used a self-developed profile grinding machine to conduct experiments. The experimental results revealed that the proposed wheel wear detection method can effectively measure the wear degree of grinding points distributed on the wheel profile and that the wheel-wear-induced contour errors can be effectively compensated to improve the machining accuracy. Furthermore, severe wheel worn can be predicted and the advance notification of wheel dressing can be achieved through real-time monitoring of the wheel-wear-induced contour errors.